nf-core/sammyseq

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Introduction

This document describes the output produced by the pipeline. Most of the plots are taken from the MultiQC report, which summarises results at the end of the pipeline.

The directories listed below will be created in the results directory after the pipeline has finished. All paths are relative to the top-level results directory.

Pipeline overview

The pipeline is built using Nextflow and processes data using the following steps:

• FastQC
• Trim reads
• Alignment on Reference
• Mark Duplicate reads
• Samtools reads filtering
• Signal track generation
• DeepTools based QC
• Comparisons
• MultiQC
• Pipeline information

Read quality check

FastQC

FastQC gives general quality metrics about the sequenced reads. It provides information about the quality score distribution across reads, per base sequence content (%A/T/G/C), adapter contamination and overrepresented sequences. For further reading and documentation see the FastQC help pages.

Trim reads

Trimmomatic is a software used to trim adapter sequences and low quality bases from the end of reads and quality check after this step is performed again with Fastqc.

Alignment on Reference

The alignment is performed using BWA and the aligned reads are then sorted by chromosome coordinates with samtools.

Mark Duplicate reads

Read pairs that are likely to have originated from duplicates of the same original DNA fragments through some artificial processes are identified. These are considered to be non-independent observations, so all but a single read pair within each set of duplicates are marked, not removed from the bam file.

Samtools reads filtering

The BAM files generated are further processed with SAMtools for filtering (based on samtools flags and quality score) and indexing, as well as to generate read mapping statistics.

Signal track generation

deepTools is used to generate single fraction signals in bigWig format, an indexed binary format useful for displaying dense, continuous data in Genome Browsers such as the UCSC and IGV. The bigWig format is also supported by various bioinformatics software for downstream processing such as meta-profile plotting. The generated signal tracks represent read coverage and can be normalized using different methods: RPKM (default option),CPM, BPM and RPGC.

DeepTools based QC

DeepTools is used to perform quality control analysis at the aligned fraction level. The pipeline uses several DeepTools commands to generate comprehensive QC metrics and visualizations.

MultiBAMSummary

The process starts with multiBamSummary, which computes the read coverage over the entire genome (or a specified region) for multiple BAM files. This creates a matrix of read counts that serves as input for the subsequent analyses. By default, the bin size is set to 50000, but you can adjust this using the —bam_binsize parameter.

deeptools/quality_control/multibamsummary/
    ${meta.id}.npz: Binary file containing the read coverage matrix
    outRawCounts.txt: Text file with raw read counts

PCA (Principal Component Analysis)

PCA is used to analyze and visualize variability in high-dimensional datasets. In the context of sequencing data analysis, PCA helps to determine if samples show greater variability between experimental conditions than between replicates of the same treatment.

deeptools/quality_control/plotpca/
    ${meta.id}.pdf: PCA plot
    ${meta.id}.tab: Table with PCA coordinates

Correlation Heatmap

The correlation analysis computes the overall similarity between samples based on read coverage. The result is visualized as a heatmap of correlation coefficients, indicating the strength of the relationship between samples. You can specify the correlation method (e.g., ‘spearman’, ‘pearson’) if the parameter qc_corr_method is provided (default is pearson)

deeptools/quality_control/plotcorrelation/
    ${meta.id}.pdf: Correlation heatmap
    ${meta.id}.tab: Table with correlation coefficients

Fingerprint Plot

The fingerprint plot helps determine how well the signal in the sample can be differentiated from the background distribution of reads in the control sample. This plot is particularly useful for assessing the strength of the experiment for factors with enrichment in well-defined and relatively narrow regions.

Two types of fingerprint plots are generated:

Global Fingerprint Plot: Covers the entire genome

deeptools/quality_control/plotfingerprint/global/
    ${meta.id}_global.pdf: Global fingerprint plot
    ${meta.id}_global.raw.txt: Raw data for the global fingerprint plot

Region-specific Fingerprint Plot: Focuses on a user-specified genomic region (if --region parameter is provided (e.g., 'chr1', 'chr2:1000000-2000000'))

deeptools/quality_control/plotfingerprint/${params.region}/
    ${meta.id}_region_${params.region}.pdf: Region-specific fingerprint plot
    ${meta.id}_region_${params.region}.raw.txt: Raw data for the region-specific fingerprint plot

Comparisons

When --comparisonFile is set, the difference between sample1 and sample2 read density profile smoothed by the Gaussian kernel is calculated and saved in bigwig format, as described in Kharchenko PK, Tolstorukov MY, Park PJ “Design and analysis of ChIP-seq experiments for DNA-binding proteins” Nat. Biotech. doi:10.1038/nbt.1508

MultiQC

MultiQC is a visualization tool that generates a single HTML report summarising all samples in your project. Most of the pipeline QC results are visualised in the report and further statistics are available in the report data directory.

Results generated by MultiQC collate pipeline QC from supported tools e.g. FastQC. The pipeline has special steps which also allow the software versions to be reported in the MultiQC output for future traceability. For more information about how to use MultiQC reports, see http://multiqc.info.

Reference genome files

A number of genome-specific files if required by some of the analysis steps. If the --save_reference parameter is provided then the alignment indices generated by the pipeline will be saved in this directory.

Pipeline information

Nextflow provides excellent functionality for generating various reports relevant to the running and execution of the pipeline. This will allow you to troubleshoot errors with the running of the pipeline, and also provide you with other information such as launch commands, run times and resource usage.